

'''simple docstring'''





import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image

				from transformers.image_utils import load_image
else:


				class  UpperCAmelCase :

						'''simple docstring'''






						@staticmethod
						def 			UpperCamelCase(					*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)  ->     List[Any]:






											'''simple docstring'''



											pass







				def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[Any]:
									return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A_	= (
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)


@is_pipeline_test
@require_torch
@require_vision
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
		@require_pytesseract
		@require_vision
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model=SCREAMING_SNAKE_CASE_   ,			tokenizer=SCREAMING_SNAKE_CASE_   ,			image_processor=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE_		)   ,			SCREAMING_SNAKE_CASE_   ,			''		)		)		)
							lowerCamelCase_  				=      'What is the placebo?'
							lowerCamelCase_  				=      [
							    {
							        'image': load_image(SCREAMING_SNAKE_CASE_		),
							        'question': question,
							    },
							    {
							        'image': image,
							        'question': question,
							    },
							    {
							        'image': image,
							        'question': question,
							        'word_boxes': word_boxes,
							    },
							]
							return dqa_pipeline, examples
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      dqa_pipeline(SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(
							    SCREAMING_SNAKE_CASE_   ,			[
							        [
							            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'answer': ANY(SCREAMING_SNAKE_CASE_		), 'start': ANY(SCREAMING_SNAKE_CASE_		), 'end': ANY(SCREAMING_SNAKE_CASE_		)},
							            {'score': ANY(SCREAMING_SNAKE_CASE_		), 'answer': ANY(SCREAMING_SNAKE_CASE_		), 'start': ANY(SCREAMING_SNAKE_CASE_		), 'end': ANY(SCREAMING_SNAKE_CASE_		)},
							        ]
							    ]
							    * 3   ,			)
		@require_torch
		@require_detectrona
		@require_pytesseract
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      pipeline('document-question-answering'   ,			model='hf-internal-testing/tiny-random-layoutlmv2'		)
							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'How many cats are there?'

							lowerCamelCase_  				=      [
							    {'score': 0.0_001, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
							    {'score': 0.0_001, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
							]
							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      dqa_pipeline({'image': image, 'question': question}   ,			top_k=2		)
							self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			SCREAMING_SNAKE_CASE_		)

							# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
							# Empty answer probably
							lowerCamelCase_  				=      './tests/fixtures/tests_samples/COCO/000000039769.png'
							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(SCREAMING_SNAKE_CASE_   ,			[]		)

							# We can optionnally pass directly the words and bounding boxes
							lowerCamelCase_  				=      './tests/fixtures/tests_samples/COCO/000000039769.png'
							lowerCamelCase_  				=      []
							lowerCamelCase_  				=      []
							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			words=SCREAMING_SNAKE_CASE_   ,			boxes=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(SCREAMING_SNAKE_CASE_   ,			[]		)
		@slow
		@require_torch
		@require_detectrona
		@require_pytesseract
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'   ,			revision='9977165'   ,			)
							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'What is the invoice number?'

							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline({'image': image, 'question': question}   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline(
							    [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        [
							            {'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
							            {'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
							        ],
							    ]
							    * 2   ,			)
		@slow
		@require_torch
		@require_detectrona
		@require_pytesseract
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'   ,			revision='9977165'   ,			max_seq_len=50   ,			)
							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'What is the invoice number?'

							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
							        {'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline({'image': image, 'question': question}   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
							        {'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline(
							    [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        [
							            {'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
							            {'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
							        ]
							    ]
							    * 2   ,			)
		@slow
		@require_torch
		@require_pytesseract
		@require_vision
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      AutoTokenizer.from_pretrained(
							    'impira/layoutlm-document-qa'   ,			revision='3dc6de3'   ,			add_prefix_space=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model='impira/layoutlm-document-qa'   ,			tokenizer=SCREAMING_SNAKE_CASE_   ,			revision='3dc6de3'   ,			)
							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'What is the invoice number?'

							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline({'image': image, 'question': question}   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline(
							    [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        [
							            {'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
							            {'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
							        ]
							    ]
							    * 2   ,			)

							lowerCamelCase_  				=      list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE_		)   ,			SCREAMING_SNAKE_CASE_   ,			''		)		)		)

							# This model should also work if `image` is set to None
							lowerCamelCase_  				=      dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
							    ]   ,			)
		@slow
		@require_torch
		@require_pytesseract
		@require_vision
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      AutoTokenizer.from_pretrained(
							    'impira/layoutlm-document-qa'   ,			revision='3dc6de3'   ,			add_prefix_space=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model='impira/layoutlm-document-qa'   ,			tokenizer=SCREAMING_SNAKE_CASE_   ,			revision='3dc6de3'   ,			max_seq_len=50   ,			)
							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'What is the invoice number?'

							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)

							lowerCamelCase_  				=      dqa_pipeline(
							    [{'image': image, 'question': question}, {'image': image, 'question': question}]   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        [
							            {'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
							            {'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
							        ]
							    ]
							    * 2   ,			)

							lowerCamelCase_  				=      list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE_		)   ,			SCREAMING_SNAKE_CASE_   ,			''		)		)		)

							# This model should also work if `image` is set to None
							lowerCamelCase_  				=      dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}   ,			top_k=2		)
							self.assertEqual(
							    nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[
							        {'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
							        {'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
							    ]   ,			)
		@slow
		@require_torch
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      pipeline(
							    'document-question-answering'   ,			model='naver-clova-ix/donut-base-finetuned-docvqa'   ,			tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa'		)   ,			feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa'   ,			)

							lowerCamelCase_  				=      INVOICE_URL
							lowerCamelCase_  				=      'What is the invoice number?'
							lowerCamelCase_  				=      dqa_pipeline(image=SCREAMING_SNAKE_CASE_   ,			question=SCREAMING_SNAKE_CASE_   ,			top_k=2		)
							self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_   ,			decimals=4		)   ,			[{'answer': 'us-001'}]		)






		@require_tf
		@unittest.skip('Document question answering not implemented in TF'		)
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							pass





"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE			  =  """cuda""" if torch.cuda.is_available() else """cpu"""



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=1_00		,   UpperCAmelCase_=" "					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		text.split(UpperCAmelCase_					)
       return [character.join(text[i : i + n]					).strip() for i in range(0		,   len(UpperCAmelCase_					)		,   UpperCAmelCase_					)]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		[], []
       for title, text in zip(documents["title"]		,   documents["text"]					):
              if text is not None:
                     for passage in split_text(UpperCAmelCase_					):
                            titles.append(title if title is not None else ""					)
                            texts.append(UpperCAmelCase_					)
       return {"title": titles, "text": texts}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				dict:







       """simple docstring"""
       UpperCamelCase       			=		ctx_tokenizer(
           documents["title"]		,   documents["text"]		,   truncation=UpperCAmelCase_		,   padding="longest"		,   return_tensors="pt"					)["input_ids"]
       UpperCamelCase       			=		ctx_encoder(input_ids.to(device=UpperCAmelCase_					)		,   return_dict=UpperCAmelCase_					).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   )->				List[str]:







       """simple docstring"""
       ######################################
       logger.info("Step 1 - Create the dataset"					)
       ######################################

       # The dataset needed for RAG must have three columns:
       # - title (string): title of the document
       # - text (string): text of a passage of the document
       # - embeddings (array of dimension d): DPR representation of the passage
       # Let's say you have documents in tab-separated csv files with columns "title" and "text"
       assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

       # You can load a Dataset object this way
       UpperCamelCase       			=		load_dataset(
           "csv"		,   data_files=[rag_example_args.csv_path]		,   split="train"		,   delimiter="\t"		,   column_names=["title", "text"]					)

       # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

       # Then split the documents into passages of 100 words
       UpperCamelCase       			=		dataset.map(UpperCAmelCase_		,   batched=UpperCAmelCase_		,   num_proc=processing_args.num_proc					)

       # And compute the embeddings
       UpperCamelCase       			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase_					)
       UpperCamelCase       			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
       UpperCamelCase       			=		Features(
           {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
       UpperCamelCase       			=		dataset.map(
           partial(UpperCAmelCase_		,   ctx_encoder=UpperCAmelCase_		,   ctx_tokenizer=UpperCAmelCase_					)		,   batched=UpperCAmelCase_		,   batch_size=processing_args.batch_size		,   features=UpperCAmelCase_		,   )

       # And finally save your dataset
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset"					)
       dataset.save_to_disk(UpperCAmelCase_					)
       # from datasets import load_from_disk
       # dataset = load_from_disk(passages_path)  # to reload the dataset

       ######################################
       logger.info("Step 2 - Index the dataset"					)
       ######################################

       # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
       UpperCamelCase       			=		faiss.IndexHNSWFlat(index_hnsw_args.d		,   index_hnsw_args.m		,   faiss.METRIC_INNER_PRODUCT					)
       dataset.add_faiss_index("embeddings"		,   custom_index=UpperCAmelCase_					)

       # And save the index
       UpperCamelCase       			=		os.path.join(rag_example_args.output_dir		,   "my_knowledge_dataset_hnsw_index.faiss"					)
       dataset.get_index("embeddings"					).save(UpperCAmelCase_					)
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class 	__a	:
       UpperCamelCase_		:       str				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''						)     ,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/rag-sequence-nq'''     ,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default='''facebook/dpr-ctx_encoder-multiset-base'''     ,  metadata={
               '''help''': (
                   '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                   ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
               )
           }     ,  )
       UpperCamelCase_		:       Optional[str]				=      field(
           default=str(Path(_lowerCAmelCase						).parent / '''test_run''' / '''dummy-kb'''						)     ,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       Optional[int]				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
           }     ,  )
       UpperCamelCase_		:       int				=      field(
           default=16     ,  metadata={
               '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
           }     ,  )





@dataclass
class 	__a	:
       UpperCamelCase_		:       int				=      field(
           default=768     ,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,  )
       UpperCamelCase_		:       int				=      field(
           default=128     ,  metadata={
               '''help''': (
                   '''The number of bi-directional links created for every new element during the HNSW index construction.'''
               )
           }     ,  )


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      SCREAMING_SNAKE_CASE			  =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE  ,   SCREAMING_SNAKE_CASE			  =  parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE			  =  rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)





